With rapid development of Internet technologies, networking and transparency of personal data of users has become an irresistible trend. Some service platforms that provide Internet services for users can collect massive user data by collecting daily generated service data of the users. The user data is a very precious “resource” for an operator of the service platform. The operator of the service platform can construct a user evaluation model based on the “resource” through data mining and machine learning, and make evaluation and decision for the user by using the user evaluation model.
For example, in a credit-based loan granting scenario, data features of several dimensions can be extracted from massive user data, training samples can be constructed based on the extracted features, and a user risk evaluation model can be constructed through training by using a specific machine learning algorithm. Then, risk evaluation is performed on a user by using the user risk evaluation model, whether the user is a risky user is determined based on a risk evaluation result, and then whether a loan needs to be granted to the user is determined.